Modelling of EEG Data for Right and Left Hand Movement
|
|
- Dominic Parker
- 6 years ago
- Views:
Transcription
1 Modelling of EEG Data for Right and Left Hand Movement Priya Varshney Electrical Engineering Department Madhav Institute of Technology and Science Gwalior, India Rakesh Narvey Electrical Engineering Department Madhav Institute of Technology and Science Gwalior, India Abstract- Brain machine Interface (BMI) improves the approach to life of traditional individuals by enhancing their performance levels.bci is used to regulate computers, robots, prosthetic devices and alternative helpful technologies for rehabilitation. Once pre-processing of the signals from their electrodes (C3 & C4), the rippling coefficients, Power Spectral Density of the alpha and also the central beta band and also the average power of the individual bands are utilized as options for classification. The aim of this study is to research the performance of linear discriminant analysis (LDA), quadratic discriminant analysis () and K- nearest neighbor () algorithms in differentiating the raw EEG knowledge obtained, into their associative movement, namely, left-right movement additionally the importance of the feature vectors elite is highlighted during this study. Our approach bestowed during this paper is sort of easy, straightforward to execute and is valid robustly with an outsized dataset. Keywords- Bel, ERS, ERD, Wavelet Coefficients, PSD, average band power estimates, LDA,,. I. INTRODUCTION Controlling a laptop or robotic device with thought solely with none physical intervention is that the principal plan behind brain laptop interfaces (BCI). BCIs use communication of the brain with outer atmosphere that does not follow brain\'s standard output pathways (i.e., through peripheral nerves and muscles). The brain activities for BCI is measured victimisation graphical record (electroencephalography), ECoG (electrocorticography), fnirs (functional close to Infrared spectroscopes), fmri (functional resonance Imaging), one thousand (magneto encephalography), LPF (Local Potential Field) [3]. Graphical record primarily based BCI is most popular because it is noninvasive, value economical, portable, and easy-to-use and provides superior temporal resolution. RAKESH NARVEY, Electrical Department, Madhav Institute Of TechnologyAndScience Gwalior,India, PRIYA VARSHNEY, Electrical Department,Madhav Institute Of TechnologyAndScienceGwalior,India, BCIs not solely improve the approach to life of the conventional folks by enhancing their performance levels, it conjointly provides how of communication for the disabled folks with their encompassing. United Nations agency are otherwise unable to physically communicate.bci is accustomed management computers, robots, prosthetic devices and alternative helpful technologies for rehabilitation. Capturing motor intention and corporal punishment the specified movement are the first basis of brain-computer interfaces for neural medical specialty. They restore the motor ability or communication to impaired people by decryption the intentions of the individual. One in every of the most analysis areas of graphical record primarily based BCI for control is to decrypt the brain signals cherish explicit limb movements [1], [2]. it's currently quick changing into a replacement tool for communication, and might be employed in sectors like artificial intelligence, mass communication, vehicles, games and recreation. The aim of this study is to research the performance of linear discriminant analysis (LDA), quadratic discriminant analysis () and K-nearest neighbor () algorithms in differentiating the raw graphical record knowledge obtained, into their associative movement, namely, left/right hand movement. Co-jointly the importance of the feature vectors hand-picked is highlighted during this study. Filtering is performed on the graphical record signals to create them free from noise, and afterwards feature extraction and classification are performed. The options thought-about during this paper embrace rippling coefficients, average band power and power spectral density. The raw dataset has been denoised by filtering, followed by feature extraction by rippling transformation, band power estimation and power spectral density strategies. In one in every of the approaches we tend to fed all the extracted options severally and within the alternative approach we tend to fed all the extracted options to LDA, and classifiers clearly to classify left and right limb movement (Fig. I). During imagination or execution of part movements, an incident connected synchronization (ERS) within the gamma band and an incident connected desynchronization (ERD) within the alphabetic character and beta band of the encephalogram originates in our brain. The gamma ERS and 1682
2 5 cm International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 6, June 2014 therefore the mu-beta ERD happens at the contralateral aspect of the brain close to sensory system and motor {area motor, region area, excitable area, cortical area, cortical region} area throughout explicit limb movement. Just in case of ERS the ability of the gamma part will increase, whereas just in case of ERD the ability of the mu-beta part decreases. [4], [5]. EEG DATA FEATURE EXTRACTION C3 1 Cz 2 C4 3 CLASSIFICATION LDA,, Fig.2: Electrode placement based on the experiment Left Hand Movement Right Hand Movement III. PRE-PROCESSING OF THE EXPERIMENTAL DATA Fig.1: Block diagram of the approach applied in this paper II. EXPERIMENTAL DATA DESCRIPTION The experimental information was obtained from BCI Competition 2003 provided by Department of Medical information processing, Institute for medical specialty Engineering, University of Technology city. This dataset was recorded from a traditional subject (female, twenty five yr) throughout a feedback session wherever the topic was created to sit down in an exceedingly quiet chair with armrests. The task was to regulate a feedback bar by suggests that of images left right movement within which the order of left and right causes were random. The recording was created employing a G.tec electronic equipment and a Ag/ AgCI conductor and 3 bipolar graph channels were measured over C3, CZ and C4 conductor (Fig.2). The experiment consists of seven sessions with in trials each conducted on an equivalent day with many minutes break m between. In every trial, the first two seconds was quite within the two second AN acoustic information indicates the start of the path with a fixation cross \'+\' displayed on the screen and at the third second the visual cue (left-right arrow) is displayed. At an equivalent time the topic was asked to maneuver the bar within the direction of the cue as feedback. The feedback was supported river parameters of channel C3 and C4 and therefore the river parameters were combined with a discriminant analysis into one output parameter (Fig.3). The graph information was sampled at 128Hz. A total of 280 trials got of nine second every. Out of the 3 electrodes used, C3 and C4 are light for this study. CZ is not noted as a result of it's of very little connection for extracting info on left-right movement [6] sec Trigger Beep Feedback period with Cue Fig.3: Timing scheme of the experiment Thus, the full dataset comprised of 1152 x two x 280 information. The trials for training and testing were elite at random to forestall any systematic result owing to feedback. So, a complete of a hundred and forty trials were elite for coaching and also the rest a hundred and forty trials for take a look at because the visual cue started from t=3 sec to t=9 sec, thus, solely the information for this point interval was elite. 1683
3 Currently it's famous that the brain electrical activities principally occur within the zero.3-40hz bands, and also the higher frequencies will be thought-about as noise supported their environments and recording techniques. Therefore a band pass filter is employed to filter within the frequency band: zero.5-30 Hz. IV DESCRIPTION OF THE FEATURES EXTRACTED domain. the ability spectral density (PSD) is outlined because the Fourier remodel (FT) of the signal's autocorrelation perform only if the signal is stationary in an exceedingly wide sense [10]. So for AN encephalogram signal segmenting the entire statistic knowledge would be a perfect approach. For this paper, the Welch approach was applied together with a performing window of length sixty four. The Welch methodology divides the day series knowledge into overlapping segments, computing a changed periodogram of every section and so the PSD estimates is averaged. A. Wavelet Features Feature Extraction Wavelet transforms may be a terribly effective thanks to extract options from AN encephalogram signal [7,8]. Their ability to discriminate each the temporal and spectral domain options of the signals makes them a vital quality for encephalogram analysis. conjointly the ripple remodel don\'t suffer from the time-frequency trade off inherent in brief Time Fourier remodel (STFT) and Fourier remodel (FT) as their multi-scale approximation permits for effective localization of the signal with numerous spectral-temporal characteristics. so for a non-stationary signal like encephalogram, it\'s a good analysis tool. The distinct ripple transforms analyzes the signals at totally different resolutions by rotten the signal into coarse approximation and detail info. Every level includes of 2 digital filters and 2 down-samplers by a pair of. The downsampled output of the first high-pass and low-pass filters provides the detail D one and approximation AI, severally. The primary approximation is any rotten and therefore the method continued, till the specified result\'s obtained. [9, 10]. In the gift study, Daubechies (db) mother ripple of order four is employed. Once trials with the encephalogram knowledge, the D3 options i.e., the third level constant for the individual electrodes were elite collectively of the feature parts for the [mal feature vector. Figure four and five shows the ripple decomposition for left-right representational process for C3 and C4 conductor. Fig. 4b: Wavelet Coefficient for Left movement for C3 electrode Fig.5a: Wavelet Coefficient for Left movement for C4 electrode Fig.4a: Wavelet Coefficient for Left movement for C3 electrode Spectral density ways extract info from a sign to explain the distribution of its power within the frequency Fig.5b: Wavelet Coefficient for Left movement for C4 electrode. B. Spectral Estimation Method 1684
4 The PSD estimates were obtained for two frequency bands, namely the alpha or mu band (8-12Hz) and the central beta band (I8-25Hz) for each respective electrode. Also the average power was obtained for each band. Then the difference of the PSD estimates (formula 1) and average power (formula 2) is selected as another feature for this study... (1). (2) Fig. 7a: PSD plot for Electrode C3 Fig. 6a: PSD plot for Electrode C4 Fig. 7b: PSD plot for Electrode C3 TABLE I. FEATURE VECTORS WITH THEIR RESPECTIVE SIZE FEATURED VECTORS SIZE (NO. of features per trials X No. of Trials ) Wavelet Coefficient (D3) 204 x 140 Alpha band PSD Estimates 768 x 140 Fig. 6b: PSD plot for Electrode C4 Alpha band Average Power 1 x 140 Beta Band PSD estimates 768 x
5 Beta Band Average Power 1 x 140 Total V. RESULTS AND DISCUSSIONS The take a look at knowledge was used for validation of the classifiers truth labels of the take a look at knowledge were obtained from the web site of the BCI competition All of the information were band pass filtered between the frequency ranges of zero.5-30hz. From the 2 electrodes of interest, namely, C3 and C4, rippling coefficients, PSD estimates for the alpha and beta bands and their corresponding powers were elite because the options for this study. The feature vectors are valid victimization paired t-test and their various likelihood of the prevalence of sort I and kind II error area unit shown in Table II every single feature vector and also the complete feature set were fed into LDA, and classifiers on an individual basis during a MA TLAB atmosphere. The results of the classification area unit shown in Table II. The error in Table II provides the misclassification error whereas coaching the dataset and also the accuracy is obtained once the take a look at knowledge is fed to the trained classifier. Fig seven illustrates the accuracy with completely different range of options for the 3 classifiers. it\'s seen that the accuracy is almost an equivalent for the various range of options taken. it's additionally ascertained from Table II that once solely the rippling constant feature vector is employed, it gave poor classification accuracy with the classifiers (i.e., LDA, and ) thanks to its complete non dimensionality rippling coefficients classification with showed highest accuracy of eightieth. The facility spectral density estimate feature vector showed higher classification accuracy with relation to rippling coefficients and average band power estimates. The complete feature vector set comprising all the extracted options with bigger spatial property indicated higher performance accuracy of eightieth, 80% and 75.71% with LDA, and severally. LDA showed higher classification with PSD vector And complete feature set with an accuracy of eightieth. performed higher with PSD vector with AN accuracy of eighty one.43%. showed highest performance with average band power estimate vector with AN accuracy of eighty four.29%. TABLE II FEATURES FEAT URES Wavelet Coeffic ient Power Spectral Density Average band power RESULT OF CLASS1F1CATION WITH THE SELECTED TYPE I ERROR TYPE II ERROR CLASS IFIER LDA LDA LDA All LDA ERROR (IN %) ACCU RA CY (IN %) Fig.9: Beta Band PSD estimate for a) left movement b) right movement VI. CONCLUSION In this paper, options are extracted from the preprocessed graph signal and fed to the motor mental imagery classifiers for differentiating the graph signal to its corresponding leftright limb movement rippling remodel, power spectral density estimate and average band power estimates are techniques followed during this study for feature extraction. In one in all the approaches we have a tendency to feed all the extracted 1686
6 options on an individual basis and in another approach we have a tendency to shape a feature vector and fed it to LDA, and algorithms clearly to classify left and right limb movement. it\'s evident from the results that thanks to the non-linearity of the rippling coefficients it contributed to poor classification accuracy once used on an individual basis once every feature vector is fed for classification, PSD showed highest accuracy than the remainder feature vectors. the whole set to feature vector comprising all the options (i.e., rippling coefficients, PSD and average band power estimate) performed higher with the classifiers while not a lot of deviation within the classification accuracy. Plenty of the classification depends on the method of the feature vectors hand-picked and therefore the parameters that outline these vectors. The process of the options needs additional validation and study to boost the accuracy of the classifiers. Also, it\'s command that the mix of feature vector is important for correct classification, so newer options ought to be tried bent on additional improve the classification of left-right motor mental imagery.our approach of feature extraction and classification given here is incredibly easy and strong to manage graph primarily based BCI devices it\'s needed to find out additional relevant options with less procedure time and with higher procedure potency. Future study during this direction can aim at techniques for optimizing feature choice, extraction and classification methodologies to be enforced in on-line classification of graph information for BCI analysis. REFERENCES [I] Schwartz A.B., Cui X.T., Weber DJ., Moran D.W. "Brain Controlled Interfaces: Movement Restoration using Neural Prosthetics." Neuron vol. 52, October 2006, pp [2] Lebedev M.A., Nicoleis, "Brain-machine interface: Past, present and future", Trends Neurosci. Vol. 29(9), September 2006, pp [3] Anderson R.A., Musallam S., Pesaran B., "Selecting the signals for a brain-machine interface", Curr Opin Neurobiol Vol.l4 (6), December 2004, pp [4] Matsunanga T., Katayama Y., Hayami T., Iramina K. "Measurement of mulbeta ERD and gamma ERS during the imagination of body parts movement." 30'h Annual International IEEE EMBS Conference Vancouver Canada, August 2008 [5] Hema C.R., Paulraj M.P., Yaacob S., Adorn AH., Nagarajan R. "Recognition of motor imagery of hand movements for a BMI using PCA features." 2008 international Conference on Electronic Design, Penang, Malaysia, December 2008 [6] Xu Huaiyu, Lou Jian, Su Ruidan, Zhang Erpang "Feature Extraction and Classification of EEG for imaging left-right hands movement." [7] Schloegl A, "Dynamic spectral analysis based on an autoregressive model with time-varying coefficients", IEEEEMBC and CMBEC, 1995 [8] Darvishi S., AI-Ani A "Brain-computer interface analysis using continuous wavelet transform and adaptive neuro-fuzzy classifier", Proc. 291h Int. Annu. Conf. IEEE Eng. Med. BioI. Soc., August 2007, pp [9] XU Q., Zhou H., Wang Y., Huang J. "Fuzzy support vector machine for classification of EEG signals using wavelet based features." Medical Engineering & Physics 31, 2009, pp [10] Herman P., Prasad G., McGinnity T.M., Coyle D. "Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification." IEEE Trans. Neural system. Rehab eng. 16(4), August 2008, pp [11] Daniella Birkel, "Regularized Discriminant Analysis" Rakesh Narvey is belonging as a Permanent faculty of Madhav Institute Of Technology and Science, Gwalior in Electrical Department. He has completed his B.E And M.E from Madhav Institute Of Technology And Science, Gwalior in 2002 and 2007respectively. He has published 8 papers in International journals/conferences. Priya Varshney is a regular student of M.E final year in the specialization of Measurement And Control in Electrical Department Of Madhav Institute Of Technology And Science, Gwalior. She has complete her B.E from ITM Gwalior in Electronics And Instrumentation in 2011.She has published 2 papers in international Journals/conference
Classification of EEG Signal for Imagined Left and Right Hand Movement for Brain Computer Interface Applications
Classification of EEG Signal for Imagined Left and Right Hand Movement for Brain Computer Interface Applications Indu Dokare 1, Naveeta Kant 2 1 Department Of Electronics and Telecommunication Engineering,
More informationMotor Imagery based Brain Computer Interface (BCI) using Artificial Neural Network Classifiers
Motor Imagery based Brain Computer Interface (BCI) using Artificial Neural Network Classifiers Maitreyee Wairagkar Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, U.K.
More informationClassification of Four Class Motor Imagery and Hand Movements for Brain Computer Interface
Classification of Four Class Motor Imagery and Hand Movements for Brain Computer Interface 1 N.Gowri Priya, 2 S.Anu Priya, 3 V.Dhivya, 4 M.D.Ranjitha, 5 P.Sudev 1 Assistant Professor, 2,3,4,5 Students
More informationOff-line EEG analysis of BCI experiments with MATLAB V1.07a. Copyright g.tec medical engineering GmbH
g.tec medical engineering GmbH Sierningstrasse 14, A-4521 Schiedlberg Austria - Europe Tel.: (43)-7251-22240-0 Fax: (43)-7251-22240-39 office@gtec.at, http://www.gtec.at Off-line EEG analysis of BCI experiments
More informationNon-Invasive Brain-Actuated Control of a Mobile Robot
Non-Invasive Brain-Actuated Control of a Mobile Robot Jose del R. Millan, Frederic Renkens, Josep Mourino, Wulfram Gerstner 5/3/06 Josh Storz CSE 599E BCI Introduction (paper perspective) BCIs BCI = Brain
More informationA Novel EEG Feature Extraction Method Using Hjorth Parameter
A Novel EEG Feature Extraction Method Using Hjorth Parameter Seung-Hyeon Oh, Yu-Ri Lee, and Hyoung-Nam Kim Pusan National University/Department of Electrical & Computer Engineering, Busan, Republic of
More informationTraining of EEG Signal Intensification for BCI System. Haesung Jeong*, Hyungi Jeong*, Kong Borasy*, Kyu-Sung Kim***, Sangmin Lee**, Jangwoo Kwon*
Training of EEG Signal Intensification for BCI System Haesung Jeong*, Hyungi Jeong*, Kong Borasy*, Kyu-Sung Kim***, Sangmin Lee**, Jangwoo Kwon* Department of Computer Engineering, Inha University, Korea*
More informationClassifying the Brain's Motor Activity via Deep Learning
Final Report Classifying the Brain's Motor Activity via Deep Learning Tania Morimoto & Sean Sketch Motivation Over 50 million Americans suffer from mobility or dexterity impairments. Over the past few
More informationRemoval of ocular artifacts from EEG signals using adaptive threshold PCA and Wavelet transforms
Available online at www.interscience.in Removal of ocular artifacts from s using adaptive threshold PCA and Wavelet transforms P. Ashok Babu 1, K.V.S.V.R.Prasad 2 1 Narsimha Reddy Engineering College,
More informationPresented by: V.Lakshana Regd. No.: Information Technology CET, Bhubaneswar
BRAIN COMPUTER INTERFACE Presented by: V.Lakshana Regd. No.: 0601106040 Information Technology CET, Bhubaneswar Brain Computer Interface from fiction to reality... In the futuristic vision of the Wachowski
More informationNoise Reduction on the Raw Signal of Emotiv EEG Neuroheadset
Noise Reduction on the Raw Signal of Emotiv EEG Neuroheadset Raimond-Hendrik Tunnel Institute of Computer Science, University of Tartu Liivi 2 Tartu, Estonia jee7@ut.ee ABSTRACT In this paper, we describe
More informationBiometric: EEG brainwaves
Biometric: EEG brainwaves Jeovane Honório Alves 1 1 Department of Computer Science Federal University of Parana Curitiba December 5, 2016 Jeovane Honório Alves (UFPR) Biometric: EEG brainwaves Curitiba
More informationNon Invasive Brain Computer Interface for Movement Control
Non Invasive Brain Computer Interface for Movement Control V.Venkatasubramanian 1, R. Karthik Balaji 2 Abstract: - There are alternate methods that ease the movement of wheelchairs such as voice control,
More informationEEG Waves Classifier using Wavelet Transform and Fourier Transform
Vol:, No:3, 7 EEG Waves Classifier using Wavelet Transform and Fourier Transform Maan M. Shaker Digital Open Science Index, Bioengineering and Life Sciences Vol:, No:3, 7 waset.org/publication/333 Abstract
More informationMove An Artificial Arm by Motor Imagery Data
International Journal of Scientific & Engineering Research Volume, Issue, June- ISSN 9-558 Move An Artificial Arm by Motor Imagery Data Rinku Roy, Amit Konar, Prof. D. N. Tibarewala, R. Janarthanan Abstract
More informationClassification of EEG Signal using Correlation Coefficient among Channels as Features Extraction Method
Indian Journal of Science and Technology, Vol 9(32), DOI: 10.17485/ijst/2016/v9i32/100742, August 2016 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 Classification of EEG Signal using Correlation
More informationAutomatic Electrical Home Appliance Control and Security for disabled using electroencephalogram based brain-computer interfacing
Automatic Electrical Home Appliance Control and Security for disabled using electroencephalogram based brain-computer interfacing S. Paul, T. Sultana, M. Tahmid Electrical & Electronic Engineering, Electrical
More informationDETECTION AND CLASSIFICATION OF POWER QUALITY DISTURBANCES
DETECTION AND CLASSIFICATION OF POWER QUALITY DISTURBANCES Ph.D. THESIS by UTKARSH SINGH INDIAN INSTITUTE OF TECHNOLOGY ROORKEE ROORKEE-247 667 (INDIA) OCTOBER, 2017 DETECTION AND CLASSIFICATION OF POWER
More informationClassification of Voltage Sag Using Multi-resolution Analysis and Support Vector Machine
Journal of Clean Energy Technologies, Vol. 4, No. 3, May 2016 Classification of Voltage Sag Using Multi-resolution Analysis and Support Vector Machine Hanim Ismail, Zuhaina Zakaria, and Noraliza Hamzah
More informationResearch Article Towards Development of a 3-State Self-Paced Brain-Computer Interface
Computational Intelligence and Neuroscience Volume 2007, Article ID 84386, 8 pages doi:10.1155/2007/84386 Research Article Towards Development of a 3-State Self-Paced Brain-Computer Interface Ali Bashashati,
More informationBrain-Machine Interface for Neural Prosthesis:
Brain-Machine Interface for Neural Prosthesis: Nitish V. Thakor, Ph.D. Professor, Biomedical Engineering Joint Appointments: Electrical & Computer Eng, Materials Science & Eng, Mechanical Eng Neuroengineering
More informationMobile robot control based on noninvasive brain-computer interface using hierarchical classifier of imagined motor commands
Mobile robot control based on noninvasive brain-computer interface using hierarchical classifier of imagined motor commands Filipp Gundelakh 1, Lev Stankevich 1, * and Konstantin Sonkin 2 1 Peter the Great
More informationWavelet Based Classification of Finger Movements Using EEG Signals
903 Wavelet Based Classification of Finger Movements Using EEG R. Shantha Selva Kumari, 2 P. Induja Senior Professor & Head, Department of ECE, Mepco Schlenk Engineering College Sivakasi, Tamilnadu, India
More informationPREDICTION OF FINGER FLEXION FROM ELECTROCORTICOGRAPHY DATA
University of Tartu Institute of Computer Science Course Introduction to Computational Neuroscience Roberts Mencis PREDICTION OF FINGER FLEXION FROM ELECTROCORTICOGRAPHY DATA Abstract This project aims
More informationBrain Computer Interfaces for Full Body Movement and Embodiment. Intelligent Robotics Seminar Kai Brusch
Brain Computer Interfaces for Full Body Movement and Embodiment Intelligent Robotics Seminar 21.11.2016 Kai Brusch 1 Brain Computer Interfaces for Full Body Movement and Embodiment Intelligent Robotics
More informationAn Improved SSVEP Based BCI System Using Frequency Domain Feature Classification
American Journal of Biomedical Engineering 213, 3(1): 1-8 DOI: 1.5923/j.ajbe.21331.1 An Improved SSVEP Based BCI System Using Frequency Domain Feature Classification Seyed Navid Resalat, Seyed Kamaledin
More informationAutomatic Fault Classification of Rolling Element Bearing using Wavelet Packet Decomposition and Artificial Neural Network
Automatic Fault Classification of Rolling Element Bearing using Wavelet Packet Decomposition and Artificial Neural Network Manish Yadav *1, Sulochana Wadhwani *2 1, 2* Department of Electrical Engineering,
More informationBRAINWAVE RECOGNITION
College of Engineering, Design and Physical Sciences Electronic & Computer Engineering BEng/BSc Project Report BRAINWAVE RECOGNITION Page 1 of 59 Method EEG MEG PET FMRI Time resolution The spatial resolution
More informationA Study of Various Feature Extraction Methods on a Motor Imagery Based Brain Computer Interface System
Basic and Clinical January 2016. Volume 7. Number 1 A Study of Various Feature Extraction Methods on a Motor Imagery Based Brain Computer Interface System Seyed Navid Resalat 1, Valiallah Saba 2* 1. Control
More informationDERIVATION OF TRAPS IN AUDITORY DOMAIN
DERIVATION OF TRAPS IN AUDITORY DOMAIN Petr Motlíček, Doctoral Degree Programme (4) Dept. of Computer Graphics and Multimedia, FIT, BUT E-mail: motlicek@fit.vutbr.cz Supervised by: Dr. Jan Černocký, Prof.
More informationNon-Invasive EEG Based Wireless Brain Computer Interface for Safety Applications Using Embedded Systems
Non-Invasive EEG Based Wireless Brain Computer Interface for Safety Applications Using Embedded Systems Uma.K.J 1, Mr. C. Santha Kumar 2 II-ME-Embedded System Technologies, KSR Institute for Engineering
More informationSignal segmentation and waveform characterization. Biosignal processing, S Autumn 2012
Signal segmentation and waveform characterization Biosignal processing, 5173S Autumn 01 Short-time analysis of signals Signal statistics may vary in time: nonstationary how to compute signal characterizations?
More informationOriginal Research Articles
Original Research Articles Researchers A.K.M Fazlul Haque Department of Electronics and Telecommunication Engineering Daffodil International University Emailakmfhaque@daffodilvarsity.edu.bd FFT and Wavelet-Based
More informationImpact of an Energy Normalization Transform on the Performance of the LF-ASD Brain Computer Interface
Impact of an Energy Normalization Transform on the Performance of the LF-ASD Brain Computer Interface Zhou Yu 1 Steven G. Mason 2 Gary E. Birch 1,2 1 Dept. of Electrical and Computer Engineering University
More informationSupplementary Information for Common neural correlates of real and imagined movements contributing to the performance of brain machine interfaces
Supplementary Information for Common neural correlates of real and imagined movements contributing to the performance of brain machine interfaces Hisato Sugata 1,2, Masayuki Hirata 1,3, Takufumi Yanagisawa
More informationEE 791 EEG-5 Measures of EEG Dynamic Properties
EE 791 EEG-5 Measures of EEG Dynamic Properties Computer analysis of EEG EEG scientists must be especially wary of mathematics in search of applications after all the number of ways to transform data is
More informationEE216B: VLSI Signal Processing. Wavelets. Prof. Dejan Marković Shortcomings of the Fourier Transform (FT)
5//0 EE6B: VLSI Signal Processing Wavelets Prof. Dejan Marković ee6b@gmail.com Shortcomings of the Fourier Transform (FT) FT gives information about the spectral content of the signal but loses all time
More informationReal Robots Controlled by Brain Signals - A BMI Approach
International Journal of Advanced Intelligence Volume 2, Number 1, pp.25-35, July, 2010. c AIA International Advanced Information Institute Real Robots Controlled by Brain Signals - A BMI Approach Genci
More informationBrain-Computer Interface for Control and Communication with Smart Mobile Applications
University of Telecommunications and Post Sofia, Bulgaria Brain-Computer Interface for Control and Communication with Smart Mobile Applications Prof. Svetla Radeva, DSc, PhD HUMAN - COMPUTER INTERACTION
More informationBCI for Comparing Eyes Activities Measured from Temporal and Occipital Lobes
BCI for Comparing Eyes Activities Measured from Temporal and Occipital Lobes Sachin Kumar Agrawal, Annushree Bablani and Prakriti Trivedi Abstract Brain computer interface (BCI) is a system which communicates
More informationA Comparison of Signal Processing and Classification Methods for Brain-Computer Interface
A Comparison of Signal Processing and Classification Methods for Brain-Computer Interface by Mark Renfrew Submitted in partial fulfillment of the requirements for the degree of Master of Science Thesis
More informationFault Location Technique for UHV Lines Using Wavelet Transform
International Journal of Electrical Engineering. ISSN 0974-2158 Volume 6, Number 1 (2013), pp. 77-88 International Research Publication House http://www.irphouse.com Fault Location Technique for UHV Lines
More informationSpeech Enhancement Based On Spectral Subtraction For Speech Recognition System With Dpcm
International OPEN ACCESS Journal Of Modern Engineering Research (IJMER) Speech Enhancement Based On Spectral Subtraction For Speech Recognition System With Dpcm A.T. Rajamanickam, N.P.Subiramaniyam, A.Balamurugan*,
More informationA Brain-Computer Interface Based on Steady State Visual Evoked Potentials for Controlling a Robot
A Brain-Computer Interface Based on Steady State Visual Evoked Potentials for Controlling a Robot Robert Prueckl 1, Christoph Guger 1 1 g.tec, Guger Technologies OEG, Sierningstr. 14, 4521 Schiedlberg,
More informationEMG feature extraction for tolerance of white Gaussian noise
EMG feature extraction for tolerance of white Gaussian noise Angkoon Phinyomark, Chusak Limsakul, Pornchai Phukpattaranont Department of Electrical Engineering, Faculty of Engineering Prince of Songkla
More informationBrain-computer Interface Based on Steady-state Visual Evoked Potentials
Brain-computer Interface Based on Steady-state Visual Evoked Potentials K. Friganović*, M. Medved* and M. Cifrek* * University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia
More informationFAULT DETECTION AND DIAGNOSIS OF HIGH SPEED SWITCHING DEVICES IN POWER INVERTER
FAULT DETECTION AND DIAGNOSIS OF HIGH SPEED SWITCHING DEVICES IN POWER INVERTER R. B. Dhumale 1, S. D. Lokhande 2, N. D. Thombare 3, M. P. Ghatule 4 1 Department of Electronics and Telecommunication Engineering,
More informationKeywords: Power System Computer Aided Design, Discrete Wavelet Transform, Artificial Neural Network, Multi- Resolution Analysis.
GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES IDENTIFICATION AND CLASSIFICATION OF POWER QUALITY DISTURBANCES BY AN EFFECTIVE WAVELET BASED NEURAL CLASSIFIER Prof. A. P. Padol Department of Electrical
More informationModeling, Architectures and Signal Processing for Brain Computer Interfaces
Modeling, Architectures and Signal Processing for Brain Computer Interfaces Jose C. Principe, Ph.D. Distinguished Professor of ECE/BME University of Florida principe@cnel.ufl.edu www.cnel.ufl.edu US versus
More informationVoice Assisting System Using Brain Control Interface
I J C T A, 9(5), 2016, pp. 257-263 International Science Press Voice Assisting System Using Brain Control Interface Adeline Rite Alex 1 and S. Suresh Kumar 2 ABSTRACT This paper discusses the properties
More informationCLASSIFICATION OF CLOSED AND OPEN-SHELL (TURKISH) PISTACHIO NUTS USING DOUBLE TREE UN-DECIMATED WAVELET TRANSFORM
CLASSIFICATION OF CLOSED AND OPEN-SHELL (TURKISH) PISTACHIO NUTS USING DOUBLE TREE UN-DECIMATED WAVELET TRANSFORM Nuri F. Ince 1, Fikri Goksu 1, Ahmed H. Tewfik 1, Ibrahim Onaran 2, A. Enis Cetin 2, Tom
More informationWavelet Transform for Classification of Voltage Sag Causes using Probabilistic Neural Network
International Journal of Electrical Engineering. ISSN 974-2158 Volume 4, Number 3 (211), pp. 299-39 International Research Publication House http://www.irphouse.com Wavelet Transform for Classification
More informationSonia Sharma ECE Department, University Institute of Engineering and Technology, MDU, Rohtak, India. Fig.1.Neuron and its connection
NEUROCOMPUTATION FOR MICROSTRIP ANTENNA Sonia Sharma ECE Department, University Institute of Engineering and Technology, MDU, Rohtak, India Abstract: A Neural Network is a powerful computational tool that
More informationHIGH IMPEDANCE FAULT DETECTION AND CLASSIFICATION OF A DISTRIBUTION SYSTEM G.Narasimharao
Vol. 1 Issue 5, July - 2012 HIGH IMPEDANCE FAULT DETECTION AND CLASSIFICATION OF A DISTRIBUTION SYSTEM G.Narasimharao Assistant professor, LITAM, Dhulipalla. ABSTRACT: High impedance faults (HIFs) are,
More informationClassification for Motion Game Based on EEG Sensing
Classification for Motion Game Based on EEG Sensing Ran WEI 1,3,4, Xing-Hua ZHANG 1,4, Xin DANG 2,3,4,a and Guo-Hui LI 3 1 School of Electronics and Information Engineering, Tianjin Polytechnic University,
More informationConstructing local discriminative features for signal classification
Constructing local discriminative features for signal classification Local features for signal classification Outline Motivations Problem formulation Lifting scheme Local features Conclusions Toy example
More informationAudio Fingerprinting using Fractional Fourier Transform
Audio Fingerprinting using Fractional Fourier Transform Swati V. Sutar 1, D. G. Bhalke 2 1 (Department of Electronics & Telecommunication, JSPM s RSCOE college of Engineering Pune, India) 2 (Department,
More informationFEATURES EXTRACTION TECHNIQES OF EEG SIGNAL FOR BCI APPLICATIONS
FEATURES EXTRACTION TECHNIQES OF EEG SIGNAL FOR BCI APPLICATIONS ABDUL-BARY RAOUF SULEIMAN, TOKA ABDUL-HAMEED FATEHI Computer and Information Engineering Department College Of Electronics Engineering,
More informationApplication of Classifier Integration Model to Disturbance Classification in Electric Signals
Application of Classifier Integration Model to Disturbance Classification in Electric Signals Dong-Chul Park Abstract An efficient classifier scheme for classifying disturbances in electric signals using
More informationA Novel Fuzzy Neural Network Based Distance Relaying Scheme
902 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 15, NO. 3, JULY 2000 A Novel Fuzzy Neural Network Based Distance Relaying Scheme P. K. Dash, A. K. Pradhan, and G. Panda Abstract This paper presents a new
More informationDecoding Brainwave Data using Regression
Decoding Brainwave Data using Regression Justin Kilmarx: The University of Tennessee, Knoxville David Saffo: Loyola University Chicago Lucien Ng: The Chinese University of Hong Kong Mentor: Dr. Xiaopeng
More informationPhysiological signal(bio-signals) Method, Application, Proposal
Physiological signal(bio-signals) Method, Application, Proposal Bio-Signals 1. Electrical signals ECG,EMG,EEG etc 2. Non-electrical signals Breathing, ph, movement etc General Procedure of bio-signal recognition
More informationCLASSIFICATION OF POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM AND S-TRANSFORM BASED ARTIFICIAL NEURAL NETWORK
CLASSIFICATION OF POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM AND S-TRANSFORM BASED ARTIFICIAL NEURAL NETWORK P. Sai revathi 1, G.V. Marutheswar 2 P.G student, Dept. of EEE, SVU College of Engineering,
More informationDetection and classification of faults on 220 KV transmission line using wavelet transform and neural network
International Journal of Smart Grid and Clean Energy Detection and classification of faults on 220 KV transmission line using wavelet transform and neural network R P Hasabe *, A P Vaidya Electrical Engineering
More information(Time )Frequency Analysis of EEG Waveforms
(Time )Frequency Analysis of EEG Waveforms Niko Busch Charité University Medicine Berlin; Berlin School of Mind and Brain niko.busch@charite.de niko.busch@charite.de 1 / 23 From ERP waveforms to waves
More informationEnhancement of Speech Signal Based on Improved Minima Controlled Recursive Averaging and Independent Component Analysis
Enhancement of Speech Signal Based on Improved Minima Controlled Recursive Averaging and Independent Component Analysis Mohini Avatade & S.L. Sahare Electronics & Telecommunication Department, Cummins
More informationA DWT Approach for Detection and Classification of Transmission Line Faults
IJIRST International Journal for Innovative Research in Science & Technology Volume 3 Issue 02 July 2016 ISSN (online): 2349-6010 A DWT Approach for Detection and Classification of Transmission Line Faults
More informationLinear Gaussian Method to Detect Blurry Digital Images using SIFT
IJCAES ISSN: 2231-4946 Volume III, Special Issue, November 2013 International Journal of Computer Applications in Engineering Sciences Special Issue on Emerging Research Areas in Computing(ERAC) www.caesjournals.org
More informationComputing with Biologically Inspired Neural Oscillators: Application to Color Image Segmentation
Computing with Biologically Inspired Neural Oscillators: Application to Color Image Segmentation Authors: Ammar Belatreche, Liam Maguire, Martin McGinnity, Liam McDaid and Arfan Ghani Published: Advances
More informationEnhancement of Speech Signal by Adaptation of Scales and Thresholds of Bionic Wavelet Transform Coefficients
ISSN (Print) : 232 3765 An ISO 3297: 27 Certified Organization Vol. 3, Special Issue 3, April 214 Paiyanoor-63 14, Tamil Nadu, India Enhancement of Speech Signal by Adaptation of Scales and Thresholds
More informationAvailable online at (Elixir International Journal) Control Engineering. Elixir Control Engg. 50 (2012)
10320 Available online at www.elixirpublishers.com (Elixir International Journal) Control Engineering Elixir Control Engg. 50 (2012) 10320-10324 Wavelet analysis based feature extraction for pattern classification
More informationAsynchronous BCI Control of a Robot Simulator with Supervised Online Training
Asynchronous BCI Control of a Robot Simulator with Supervised Online Training Chun Sing Louis Tsui and John Q. Gan BCI Group, Department of Computer Science, University of Essex, Colchester, CO4 3SQ, United
More informationElectroencephalographic Signal Processing and Classification Techniques for Noninvasive Motor Imagery Based Brain Computer Interface
Georgia Southern University Digital Commons@Georgia Southern Electronic Theses & Dissertations Graduate Studies, Jack N. Averitt College of Spring 2017 Electroencephalographic Signal Processing and Classification
More informationBRAIN COMPUTER INTERFACE (BCI) RESEARCH CENTER AT SRM UNIVERSITY
BRAIN COMPUTER INTERFACE (BCI) RESEARCH CENTER AT SRM UNIVERSITY INTRODUCTION TO BCI Brain Computer Interfacing has been one of the growing fields of research and development in recent years. An Electroencephalograph
More informationBCI THE NEW CLASS OF BIOENGINEERING
BCI THE NEW CLASS OF BIOENGINEERING By Krupali Bhatvedekar ABSTRACT A brain-computer interface (BCI), which is sometimes called a direct neural interface or a brainmachine interface, is a device that provides
More informationImprovement of Classical Wavelet Network over ANN in Image Compression
International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869 (O) 2454-4698 (P), Volume-7, Issue-5, May 2017 Improvement of Classical Wavelet Network over ANN in Image Compression
More informationApplication of Wavelet Transform in Power System Analysis and Protection
Application of Wavelet Transform in Power System Analysis and Protection Neha S. Dudhe PG Scholar Shri Sai College of Engineering & Technology, Bhadrawati-Chandrapur, India Abstract This paper gives a
More informationAN ANN BASED FAULT DETECTION ON ALTERNATOR
AN ANN BASED FAULT DETECTION ON ALTERNATOR Suraj J. Dhon 1, Sarang V. Bhonde 2 1 (Electrical engineering, Amravati University, India) 2 (Electrical engineering, Amravati University, India) ABSTRACT: Synchronous
More informationBrain Machine Interface for Wrist Movement Using Robotic Arm
Brain Machine Interface for Wrist Movement Using Robotic Arm Sidhika Varshney *, Bhoomika Gaur *, Omar Farooq*, Yusuf Uzzaman Khan ** * Department of Electronics Engineering, Zakir Hussain College of Engineering
More informationIdentification and Use of PSD-Derived Features for the Contextual Detection and Classification of EEG Epileptiform Transients
Clemson University TigerPrints All Theses Theses 8-2016 Identification and Use of PSD-Derived Features for the Contextual Detection and Classification of EEG Epileptiform Transients Sharan Rajendran Clemson
More informationAutomatic bearing fault classification combining statistical classification and fuzzy logic
Automatic bearing fault classification combining statistical classification and fuzzy logic T. Lindh, J. Ahola, P. Spatenka, A-L Rautiainen Tuomo.Lindh@lut.fi Lappeenranta University of Technology Lappeenranta,
More informationA Review of SSVEP Decompostion using EMD for Steering Control of a Car
A Review of SSVEP Decompostion using EMD for Steering Control of a Car Mahida Ankur H 1, S. B. Somani 2 1,2. MIT College of Engineering, Kothrud, Pune, India Abstract- Recently the EEG based systems have
More informationEE M255, BME M260, NS M206:
EE M255, BME M260, NS M206: NeuroEngineering Lecture Set 6: Neural Recording Prof. Dejan Markovic Agenda Neural Recording EE Model System Components Wireless Tx 6.2 Neural Recording Electrodes sense action
More informationClassification of Hand Gestures using Surface Electromyography Signals For Upper-Limb Amputees
Classification of Hand Gestures using Surface Electromyography Signals For Upper-Limb Amputees Gregory Luppescu Stanford University Michael Lowney Stanford Univeristy Raj Shah Stanford University I. ITRODUCTIO
More informationFACE RECOGNITION USING NEURAL NETWORKS
Int. J. Elec&Electr.Eng&Telecoms. 2014 Vinoda Yaragatti and Bhaskar B, 2014 Research Paper ISSN 2319 2518 www.ijeetc.com Vol. 3, No. 3, July 2014 2014 IJEETC. All Rights Reserved FACE RECOGNITION USING
More informationFourier and Wavelets
Fourier and Wavelets Why do we need a Transform? Fourier Transform and the short term Fourier (STFT) Heisenberg Uncertainty Principle The continues Wavelet Transform Discrete Wavelet Transform Wavelets
More informationFeature analysis of EEG signals using SOM
1 Portál pre odborné publikovanie ISSN 1338-0087 Feature analysis of EEG signals using SOM Gráfová Lucie Elektrotechnika, Medicína 21.02.2011 The most common use of EEG includes the monitoring and diagnosis
More informationEmpirical Mode Decomposition: Theory & Applications
International Journal of Electronic and Electrical Engineering. ISSN 0974-2174 Volume 7, Number 8 (2014), pp. 873-878 International Research Publication House http://www.irphouse.com Empirical Mode Decomposition:
More informationNEURALNETWORK BASED CLASSIFICATION OF LASER-DOPPLER FLOWMETRY SIGNALS
NEURALNETWORK BASED CLASSIFICATION OF LASER-DOPPLER FLOWMETRY SIGNALS N. G. Panagiotidis, A. Delopoulos and S. D. Kollias National Technical University of Athens Department of Electrical and Computer Engineering
More informationContent Based Image Retrieval Using Color Histogram
Content Based Image Retrieval Using Color Histogram Nitin Jain Assistant Professor, Lokmanya Tilak College of Engineering, Navi Mumbai, India. Dr. S. S. Salankar Professor, G.H. Raisoni College of Engineering,
More informationCharacterization of Voltage Sag due to Faults and Induction Motor Starting
Characterization of Voltage Sag due to Faults and Induction Motor Starting Dépt. of Electrical Engineering, SSGMCE, Shegaon, India, Dépt. of Electronics & Telecommunication Engineering, SITS, Pune, India
More informationAnalysis of brain waves according to their frequency
Analysis of brain waves according to their frequency Z. Koudelková, M. Strmiska, R. Jašek Abstract The primary purpose of this article is to show and analyse the brain waves, which are activated during
More informationSpectro-Temporal Methods in Primary Auditory Cortex David Klein Didier Depireux Jonathan Simon Shihab Shamma
Spectro-Temporal Methods in Primary Auditory Cortex David Klein Didier Depireux Jonathan Simon Shihab Shamma & Department of Electrical Engineering Supported in part by a MURI grant from the Office of
More informationTRANSIENT STABILITY ENHANCEMENT OF POWER SYSTEM USING INTELLIGENT TECHNIQUE
TRANSIENT STABILITY ENHANCEMENT OF POWER SYSTEM USING INTELLIGENT TECHNIQUE K.Satyanarayana 1, Saheb Hussain MD 2, B.K.V.Prasad 3 1 Ph.D Scholar, EEE Department, Vignan University (A.P), India, ksatya.eee@gmail.com
More informationLabVIEW Based Condition Monitoring Of Induction Motor
RESEARCH ARTICLE OPEN ACCESS LabVIEW Based Condition Monitoring Of Induction Motor 1PG student Rushikesh V. Deshmukh Prof. 2Asst. professor Anjali U. Jawadekar Department of Electrical Engineering SSGMCE,
More informationMURDOCH RESEARCH REPOSITORY
MURDOCH RESEARCH REPOSITORY http://dx.doi.org/10.1109/kes.1999.820143 Zaknich, A. and Attikiouzel, Y. (1999) The classification of sheep and goat feeding phases from acoustic signals of jaw sounds. In:
More informationExamination of Single Wavelet-Based Features of EHG Signals for Preterm Birth Classification
IAENG International Journal of Computer Science, :, IJCS Examination of Single Wavelet-Based s of EHG Signals for Preterm Birth Classification Suparerk Janjarasjitt, Member, IAENG, Abstract In this study,
More informationDwt-Ann Approach to Classify Power Quality Disturbances
Dwt-Ann Approach to Classify Power Quality Disturbances Prof. Abhijit P. Padol Department of Electrical Engineering, abhijit.padol@gmail.com Prof. K. K. Rajput Department of Electrical Engineering, kavishwarrajput@yahoo.co.in
More informationNeural Network Classifier and Filtering for EEG Detection in Brain-Computer Interface Device
Neural Network Classifier and Filtering for EEG Detection in Brain-Computer Interface Device Mr. CHOI NANG SO Email: cnso@excite.com Prof. J GODFREY LUCAS Email: jglucas@optusnet.com.au SCHOOL OF MECHATRONICS,
More informationAdaptive Feature Analysis Based SAR Image Classification
I J C T A, 10(9), 2017, pp. 973-977 International Science Press ISSN: 0974-5572 Adaptive Feature Analysis Based SAR Image Classification Debabrata Samanta*, Abul Hasnat** and Mousumi Paul*** ABSTRACT SAR
More information